Data-driven or supervised machine-learning algorithms are emerging as important tools for information analysis in portable devices, the cloud, and other computing devices. Machine learning involves various algorithms that can automatically learn over time. The foundation of these algorithms is built on mathematics and statistics that can be employed to predict events, classify entities, diagnose problems, and model function approximations. Applications of these algorithms include semantic text analysis, web search, and speech and object recognition, just to name a few examples. Supervised machine-learning algorithms typically operate in two phases: training and testing. In the training phase, typical input examples are used to build decision models that characterize the data. In the testing phase, the learned model is applied to new data instances in order to infer different properties such as relevance and similarity.